The software provides a generic deep learning processor IP core that is target-independent and can be deployed to any custom platform that you specify. The processor can be reused and shared to accommodate deep neural networks that have various layer sizes and parameters. Use this processor to rapidly prototype deep neural networks from MATLAB®, and then deploy the network to FPGAs.
This figure shows the deep learning processor architecture.
To illustrate the deep learning processor architecture, consider an image classification example.
You can store the input images, the weights, and the output images in the external DDR memory. The processor consists of four AXI4 Master interfaces that communicate with the external memory. Using one of the AXI4 Master interfaces, you can load the input images onto the Block RAM (BRAM). The Block RAM provides the activations to the Generic Convolution Processor.
The Generic Convolution Processor performs the equivalent operation of one convolution layer. Using another AXI4 Master interface, the weights for the convolution operation are provided to the Generic Convolution Processor. The Generic Convolution Processor then performs the convolution operation on the input image and provides the activations for the Activation Normalization. The processor is generic because it can support tensors and shapes of various sizes.
Based on the neural network that you provide, the Activation Normalization module serves the purpose of adding the ReLU nonlinearity, a maxpool layer, or performs Local Response Normalization (LRN). You see that the processor has two Activation Normalization units. One unit follows the Generic Convolution Processor. The other unit follows the Generic FC Processor.
Depending on the number of convolution layers that you have in your pretrained network, the Conv Controller (Scheduling) acts as ping-pong buffers. The Generic Convolution Processor and Activation Normalization can process one layer at a time. To process the next layer, the Conv Controller (Scheduling) moves back to the BRAM and then performs the convolution and activation normalization operations for all convolution layers in the network.
The Generic FC Processor performs the equivalent operation of one fully-connected layer (FC). Using another AXI4 Master interface, the weights for the fully-connected layer are provided to the Generic FC Processor. The Generic FC Processor then performs the fully-connected layer operation on the input image and provides the activations for the Activation Normalization module. This processor is also generic because it can support tensors and shapes of various sizes.
The FC Controller (Scheduling) works similar to the Conv Controller (Scheduling). The FC Controller (Scheduling) coordinates with the FIFO to act as ping-pong buffers for performing the fully-connected layer operation and Activation Normalization depending on the number of FC layers, and ReLU, maxpool, or LRN features that you have in your neural network. After the Generic FC Processor and Activation Normalization modules process all the frames in the image, the predictions or scores are transmitted through the AXI4 Master interface and stored in the external DDR memory.
One application of the custom deep learning processor IP core is the MATLAB controlled deep learning processor. To create this processor, integrate the deep learning processor IP with the HDL Verifier™ MATLAB as AXI Master IP by using the AXI4 slave interface. Through a JTAG or PCI express interface, you can import various pretrained neural networks from MATLAB, execute the operations specified by the network in the deep learning processor IP, and return the classification results to MATLAB.
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